ModelVersionProps

Properties for defining a ModelVersion.

See https://www.alibabacloud.com/help/ros/developer-reference/aliyun-pai-modelversion

Initializer

import "github.com/alibabacloud-go/ros-cdk/alicloudroscdkpai"
&alicloudroscdkpai.ModelVersionProps {
    ModelId: interface{},
    Uri: interface{},
    ApprovalStatus: interface{},
    ExtraInfo: interface{},
    FormatType: interface{},
    FrameworkType: interface{},
    InferenceSpec: interface{},
    Labels: interface{},
    Metrics: interface{},
    Options: interface{},
    SourceId: interface{},
    SourceType: interface{},
    TrainingSpec: interface{},
    VersionDescription: interface{},
    VersionName: interface{},
}

Properties

ModelId^Required

ModelId interface{}

• Type: interface{}

Property modelId: Model ID.

For details about how to obtain the model ID.

Uri^Required

Uri interface{}

• Type: interface{}

Property uri: Model version URI, that is, the location where the model is stored.

Possible values are:

• The HTTP(S) address of the model, for example: 'https://myweb.com/mymodel.tar.gz '.
• If the model is stored in OSS, the format is 'oss://./object '. For the endpoint configuration, see Access domain name and data center. For example, see OSS:// mybucket/'.

ApprovalStatus^Optional

ApprovalStatus interface{}

• Type: interface{}

Property approvalStatus: Admission status, with values as follows: - Pending: to be determined.

• Approved: Allow to go online.
• Rejected: Online is not allowed.

ExtraInfo^Optional

ExtraInfo interface{}

• Type: interface{}

Property extraInfo: The additional information.

FormatType^Optional

FormatType interface{}

• Type: interface{}

Property formatType: Model format, possible values: - OfflineModel - SavedModel - Keras H5 - Frozen Pb - Caffe Prototxt - TorchScript - XGBoost - PMML - AlinkModel - ONNX.

FrameworkType^Optional

FrameworkType interface{}

• Type: interface{}

Property frameworkType: Model framework, possible values: - Pytorch - XGBoost - Keras - Caffe - Alink - Xflow - TensorFlow.

InferenceSpec^Optional

InferenceSpec interface{}

• Type: interface{}

Property inferenceSpec: Describes how to apply to downstream inference services.

For example, describe the processor and container of EAS. Example: { "processor": "tensorflow_gpu_1.12" }

Labels^Optional

Labels interface{}

• Type: interface{}

Property labels: List of model version labels.

Metrics^Optional

Metrics interface{}

• Type: interface{}

Property metrics: The metrics for the model.

The length after serialization is limited to 8,192. Example: { "Results": [{ "Dataset": { "DatasetId": "d-sdkjanksaklerhfd" }, "Metrics": { "cer": 0.175 } }, { "Dataset": { "Uri": "oss://xxxx/" }, "Metrics": { "cer": 0.172 } }] }

Options^Optional

Options interface{}

• Type: interface{}

Property options: The extended field.

This is a JSON string.

SourceId^Optional

SourceId interface{}

• Type: interface{}

Property sourceId: Source ID.

• When the source type is Custom, this field is not restricted.

• When the source is PAIFlow or TrainingService, the format is: region=,workspaceId=,kind=,id= Among them:

• region is the Alibaba Cloud region ID.

• workspaceId indicates the workspace ID.
• kind: is a type. Value: PipelineRun(PAIFlow workflow);ServiceJob (training service).
• id: is a unique identifier.

SourceType^Optional

SourceType interface{}

• Type: interface{}

Property sourceType: Model source type, possible values: - Custom: Custom.

• PAIFlow:PAI workflow.
• TrainingService:PAI training service.

TrainingSpec^Optional

TrainingSpec interface{}

• Type: interface{}

Property trainingSpec: Training configuration.

Configuration for fine-tuning, incremental training.

VersionDescription^Optional

VersionDescription interface{}

• Type: interface{}

Property versionDescription: Model version description.

VersionName^Optional

VersionName interface{}

• Type: interface{}

Property versionName: The model version, which is unique for each model.

If you leave this parameter empty, the first version is 0.1.0 by default. After that, the minor version number is increased by 1 in sequence. For example, the second version number is 0.2.0. A version number consists of a major version number, a minor version number, and a stage version number, separated by periods (.). The major version number and minor version number are numeric. The stage version number begins with a digit and can include numbers, underscores, and letters. For example, the version number is 1.1.0 or 2.3.4_beta.